Improved traffic detection with support vector machine based on restricted Boltzmann machine

• Barman D, Claffy K, Faloutsos M, Fomenkov M, Kim H, Lee K (2008) Internet traffic classification demystified: myths, caveats, and the best practices. In: ACM SIGCOMM conference on emerging networking experiments and technologies (CoNEXT), ACM SIGCOMM conference on emerging networking experiments and technologies (CoNEXT), New York

• Calcagn A, Lombardi L, Pascali E (2014) Non-convex fuzzy data and fuzzy statistics: a first descriptive approach to data analysis. Soft Comput 18(8):1575–1588. doi:10.1007/s00500-013-1164-x

  Article  MATH  Google Scholar 

• Carreira-Perpinan MA, Hinton GE (2005) On contrastive divergence learning. In: Proceedings of the 10th international workshop on artificial intelligence and statistics. Society for Artificial Intelligence and Statistics, NP, pp 33–40

• Fiore U, Palmieri F, Castiglione A, Santis AD (2013) Network anomaly detection with the restricted Boltzmann machine. Neurocomputing. In: Advances in cognitive and ubiquitous computing. Selected papers from the Sixth international conference on innovative mobile and internet services in ubiquitous computing (IMIS-2012), vol 122, pp 13–23. doi:10.1016/j.neucom.2012.11.050

• Gaddam S, Phoha V, Balagani K (2007) K-means+id3: a novel method for supervised anomaly detection by cascading k-means clustering and id3 decision tree learning methods. Knowl Data Eng IEEE Trans 19(3):345–354. doi:10.1109/TKDE.2007.44

  Article  Google Scholar 

• Ghosh AK, Schwartzbard A (1999) A study in using neural networks for anomaly and misuse detection. In: Proceedings of the 8th conference on USENIX security symposium, USENIX Association, Berkeley, CA, USA, SSYM’99, vol 8, pp 12–12

• Gu Y, McCallum A, Towsley D (2005) Detecting anomalies in network traffic using maximum entropy estimation. In: Proceedings of the 5th ACM SIGCOMM conference on internet measurement, USENIX Association, Berkeley, CA, USA, IMC ’05, pp 32–32

• Hinton G (2012) A practical guide to training restricted Boltzmann machines. In: Neural networks: tricks of the trade, lecture notes in computer science, vol 7700. Springer, Berlin, Heidelberg, pp 599–619. doi:10.1007/978-3-642-35289-8_32

• Hinton GE, Osindero S, Teh Y (2006) A fast learning algorithm for deep belief nets. Neural Comput 18:1527–1554

• Hopfield JJ (1982) Neural networks and physical systems with emergent collective computational abilities. Proc Natl Acad Sci USA 79(8):2554–2558

• Kruegel C, Mutz D, Robertson W, Valeur F (2003) Bayesian event classification for intrusion detection. In: Proceedings of the 19th annual computer security applications conference, pp 14–23, doi:10.1109/CSAC.2003.1254306

• Lakhina A, Crovella M, Diot C (2005) Mining anomalies using traffic feature distributions. SIGCOMM Comput Commun Rev 35(4):217–228. doi:10.1145/1090191.1080118

  Article  Google Scholar 

• Larochelle H, Bengio Y (2008) Classification using discriminative restricted Boltzmann machines. In: Proceedings of the 25th international conference on machine learning, ACM, New York, NY, USA, ICML ’08, pp 536–543. doi:10.1145/1390156.1390224

• Le Roux N, Bengio Y (2008) Representational power of restricted Boltzmann machines and deep belief networks. Neural Comput 20(6):1631–1649. doi:10.1162/neco.2008.04-07-510

  Article  MathSciNet  MATH  Google Scholar 

• Lee W, Stolfo SJ (1998) Data mining approaches for intrusion detection. In: Proceedings of the 7th conference on USENIX security symposium, USENIX Association, Berkeley, CA, USA, SSYM’98, vol 7, pp 6–6

• Lee H, Pham P, Largman Y, Ng AY (2009) Unsupervised feature learning for audio classification using convolutional deep belief networks. In: Bengio Y, Schuurmans D, Lafferty J, Williams C, Culotta A (eds) Advances in neural information processing systems, vol 22. Curran Associates Inc, pp 1096–1104

• Li J, Kim K (2010) Hidden attribute-based signatures without anonymity revocation. Inf Sci 180(9):1681–1689. doi:10.1016/j.ins.2010.01.008

  Article  MathSciNet  MATH  Google Scholar 

• Li J, Chen X, Li M, Li J, Lee P, Lou W (2014a) Secure deduplication with efficient and reliable convergent key management. Parallel Distrib Syst IEEE Trans 25(6):1615–1625. doi:10.1109/TPDS.2013.284

  Article  Google Scholar 

• Li J, Huang X, Li J, Chen X, Xiang Y (2014b) Securely outsourcing attribute-based encryption with checkability. Parallel Distrib Syst IEEE Trans 25(8):2201–2210. doi:10.1109/TPDS.2013.271

  Article  Google Scholar 

• Mahoney MV, Chan PK (2002) Learning nonstationary models of normal network traffic for detecting novel attacks. In: Proceedings of the eighth ACM SIGKDD international conference on knowledge discovery and data mining, ACM, New York, NY, USA, KDD ’02, pp 376–385. doi:10.1145/775047.775102

• Montufar G, Ay N (2011) Refinements of universal approximation results for deep belief networks and restricted Boltzmann machines. Neural Comput 23(5):1306–1319. doi:10.1162/NECO_a_00113

• Montufar GF, Rauh J, Ay N (2011) Expressive power and approximation errors of restricted Boltzmann machines. In: Shawe-Taylor J, Zemel R, Bartlett P, Pereira F, Weinberger K (eds) Advances in neural information processing systems, vol 24. Curran Associates Inc, pp 415–423

• Munz G, Li S, Carle G (2007) Traffic anomaly detection using kmeans clustering. In. GI/ITG workshop MMBnet

• Palmieri F, Fiore U, Castiglione A, Santis AD (2013) On the detection of card-sharing traffic through wavelet analysis and support vector machines. Appl Soft Comput 13(1):615–627. doi:10.1016/j.asoc.2012.08.045

• Palmieri F, Fiore U, Castiglione A (2014) A distributed approach to network anomaly detection based on independent component analysis. Concur Comput: Pract Exp 26(5):1113–1129. doi:10.1002/cpe.3061

  Article  Google Scholar 

• Ranzato M, lan Boureau Y, Cun YL (2008) Sparse feature learning for deep belief networks. In: Platt J, Koller D, Singer Y, Roweis S (eds) Advances in neural information processing systems, vol 20. Curran Associates Inc, pp 1185–1192

• Rao X, Dong CX, Yang SQ (2003) An intrusion detection system based on support vector machine. J Softw 14(4):798–803

• Romero A, Gatta C (2013) Do we really need all these neurons. In: Sanches J, Mic L, Cardoso J (eds) Pattern recognition and image analysis, lecture notes in computer science, vol 7887. Springer, Berlin, Heidelberg, pp 460–467. doi:10.1007/978-3-642-38628-2_54

• Sancho-Asensio A, Orriols-Puig A, Golobardes E (2014) Robust on-line neural learning classifier system for data stream classification tasks. Soft Comput 18(8):1441–1461. doi:10.1007/s00500-014-1233-9

  Article  Google Scholar 

• Sinclair C, Pierce L, Matzner S (1999) An application of machine learning to network intrusion detection. In: Proceedings of the 15th annual computer security applications conference, (ACSAC ’99), pp 371–377. doi:10.1109/CSAC.1999.816048

• Wang Z, Crammer K, Vucetic S (2012) Breaking the curse of kernelization: budgeted stochastic gradient descent for large-scale svm training. J Mach Learn Res 13(1):3103–3131

  MathSciNet  MATH  Google Scholar 

• Yu J, Lee H, Kim MS, Park D (2008) Traffic flooding attack detection with snmp mib using svm. Comput Commun 31(17):4212–4219. doi:10.1016/j.comcom.2008.09.018

  Article  Google Scholar 

• Zhang J, Zulkernine M (2006) A hybrid network intrusion detection technique using random forests. In: The first international conference on availability, reliability and security, 2006. ARES 2006, pp 8–16. doi:10.1109/ARES.2006.7

• Zhang J, Xiang Y, Wang Y, Zhou W, Xiang Y, Guan Y (2013) Network traffic classification using correlation information. Parallel Distrib Syst IEEE Trans 24(1):104–117. doi:10.1109/TPDS.2012.98

  Article  Google Scholar 

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